Embodiments of the invention relate generally to automated angiogram analysis, and more specifically, to bidirectional blood vessel segmentation using top-down and bottom-up models.
Cardiac catheterization with coronary angiogram is a test commonly used to check the blood flow in coronary arteries. Physicians use angiograms to grade the severity of coronary artery disease by detecting arterial lesions. An analysis of the number, severity, and location of the lesions guides physicians in choosing between a heart bypass operation or angioplasty. Metrics such as the syntax score have been developed to prospectively characterize the coronary vasculature with respect to the number of lesions and their functional impact, location, and complexity. In general, a higher syntax score is indicative of more complex coronary artery disease that represents a bigger therapeutic challenge.
Typically, angiography image data includes the presence of a non-trivial amount of scanning noise, background clutter (e.g. tissues, vertebra, lungs), as well as large appearance variations across different scanners and patients. Accordingly, accurate localization and quantification of blood vessels reflected in X-ray angiograms, or angiography images, plays a significant role in automatic angiogram analysis.
One blood vessel segmentation in angiograms images, referred to as a top-down approach, uses prior knowledge about the blood vessels, such as shape and intensity, to guide the segmentation. Examples of top-down methods include region growing, active contour, tracking based or supervised learning approaches.
Other known blood vessel segmentations methods, referred to as bottom-up methods, segment the angiogram images into regions based on features such as edges or intensities and then identify regions correspond to the blood vessel. Examples of bottom-up methods include, graph-cut based methods or fitter based methods, such as the morphological filter or multi-scale Hessian filter.